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@article{193906,
        author = {Dr. Abdul Rahiman Sheik and Gandham Jahnavi and Chandragiri Venkata Durgarao and Shaik Subhani and Nakka Mahendra},
        title = {Stacking Classifier and LightGBM Based Prediction System for Heart Disease and Parkinson’s},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {2266-2274},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193906},
        abstract = {This project proposes a unified machine learning–based prediction system for both heart disease and Parkinson’s disease within a single platform. For heart disease prediction, an ensemble Stacking Classifier is implemented by combining multiple base models such as Random Forest, Logistic Regression, XGBoost, Support Vector Classifier, and Decision Tree, followed by an additional stacking layer with CatBoost, LightGBM, Extra Trees, and Multi-Layer Perceptron to enhance accuracy and stability. For Parkinson’s disease prediction, supervised learning algorithms including Logistic Regression, LightGBM, K-Nearest Neighbors, and Support Vector Machine are applied using extracted signal-based features. The system utilizes structured clinical datasets and statistical features, along with appropriate data preprocessing, feature selection, model training, and performance evaluation to ensure reliable predictions. The application is developed using Flask for backend processing and HTML, CSS, and JavaScript for the user interface, providing modules for user registration, login, disease selection, and result display. Overall, the project demonstrates the effective use of ensemble learning and gradient boosting techniques for multi-disease prediction in an integrated machine learning framework.},
        keywords = {Heart Disease, Parkinson’s Disease, StackingClassifier, LightGBM, Ensemble Learning, Classification, Machine Learning, Flask, Prediction System, Healthcare Data},
        month = {March},
        }